Research Engineer (Инженер-исследователь)
Вакансия № 27322814 в населенном пункте (городе) Москва, Россия от компании "Пану Ольга Германовна" на сайте Электронный Центр Занятости Населения (ЦЗН) Москвы.
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Обязательное требование к опыту работы искомого сотрудника: 3–6 лет.
График работы: полный день.
Тип занятости: полная занятость.
Вакансия № 27322814 добавлена в базу данных: Вторник, 23 сентября 2025 года.
Дата обновления этого объявления: Пятница, 26 сентября 2025 года.
| Рейтинг вакансии: 22,72 из 100 баллов |
Работодатель может предложить примерно следующую оплату труда: от 2500 USD на вакантной должности "Research Engineer (Инженер-исследователь)".
The company develops a system capable of semantic processing of programming code, e.g., code analysis, transformation, and synthesis, as well as operating a computational environment. The approach combines explicit knowledge representation in discrete structures with ML-assisted algorithms to manipulate those structures. The system we design should be able to follow the reasoning trajectory and examine the internal logic; identify a lack of information and interact with external sources; incorporate new knowledge at later stages equally inexpensive as at the initial ones.
What we need help with
1. Architecture. Designing system architecture that accounts for both theoretical developments and practical considerations.
2. Algorithms. Developing algorithms from theoretical descriptions and mathematics. Establishing exact training protocols for ML models and designing requirements for Engineers work.
3. Prototyping. Designing prototypes and proof-of-concept level solutions, that demonstrate core features of the company technology.
4. Assessment of feasibility. Serve as a constant communication channel between Research Scientists representing the theoretical side of things and Software/ML Engineers representing the practical side of things. Assess which proposed solutions and approaches are feasible from both sides' perspectives.
5. Maintaining systemic vision. Answering clarifying questions about all parts of the system, keeping knowledge about solutions up to date, identifying blind spots and problems with system design.
6. Balancing conventional and unconventional. Analysing and deciding which parts of the system should be designed and implemented using pre-existing algorithms and solutions and which require exploration. Similarly deciding on which problems should be solved by using Machine Learning and which by classical algorithms and data structures. Finding the optimal balance between interpretability, corrigibility, transparency and performance, feasibility, attainability.
Who we are looking for
1. 5+ year experience as Research Engineer
2. Experience in several of these areas: Program synthesis, Applied category theory, Knowledge Representation, Functional programming, Discrete Math, Applied Statistics, Algorithmic Complexity, Formal logic, Machine Learning, Transformers, GNN.
3. Languages: Python, Rust, C++, Haskell, OCaml, F#, Scala.
4. Master’s degree or higher.
5. English C1 or higher.
What soft skills are essential
1. Commitment. Make and fulfil commitments on time and at a high-quality level.
2. All-Round. Ability to figure out how to do new tasks from adjacent specialisations.
3. Startup Mindset. Focus on the objective, looking for new approaches, fast forwarding orientation.
4. Constructive Discussion. Presenting own arguments, listening to others' arguments, developing the next steps to resolve the difficulty.
Not just an ML
People tend to think that AI is solely Machine Learning, like ChatGPT (or DALL-E 2, or Microsoft Bing, or ChatSonic). We are building our solution on an alternative architecture which incorporates ML as a part.
Going beyond
We put much effort into profound research and development of new underlying technologies rather than reusing existing ones. We like to explore unfairly neglected and non-conventional approaches to AI.
Safety and Alignment
We want to be responsible in our research and development. We don't turn our noses up at AI safety research and take the issue of alignment seriously.
Good Salary
That is, of course, a very basic condition. We want our colleagues to be healthy, happy and recreated.
Hybrid Remote
Our team members work from both the office and homes all around the world. We are OK with that. However, we like to gather sometimes at the office to get on the same page.
Flexible Schedule
Outside scheduled team meetings, teammates are free to work on their tasks independently. When to work is a personal choice. Just do not overwork – that is inefficient.
Low Bureaucracy
What we value most are performance and results, not a strict process following. However, metrics, processes, and documentation are important too. We just keep their priorities low.
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